1 code implementation • 18 Jun 2022 • Kyung-Su Kim, Seong Je Oh, Ju Hwan Lee, Myung Jin Chung
The proposed method based on unsupervised learning improves the patient-level anomaly detection by 10% (area under the curve, 0. 959) compared with a gold standard based on supervised learning (area under the curve, 0. 848), and it localizes the anomaly region with 93% accuracy, demonstrating its high performance.
1 code implementation • 18 Jun 2022 • Kyung-Su Kim, Ju Hwan Lee, Seong Je Oh, Myung Jin Chung
The proposed CDTS-based AI CAD system yielded sensitivities of 0. 782 and 0. 785 and accuracies of 0. 895 and 0. 837 for the performance of detecting tuberculosis and pneumonia, respectively, against normal subjects.